We deploy a vanilla feature encoder, C3D [1]} pretrained on Sport-1M [2]} or I3D pretrained on Kinetics-400 [3]} to extract features for generator training. We densely sample 16 frames per clip most of the times but 12 frames per clip for I3D on UCF-Crime. After extracting the features, sparse continuous sampling is applied to sample the \(L \cdot T\)  clips to form bags of features \(\overline{B}\) . Then, \(\mathcal {L}_{MIL}\)  is deployed to optimize the generator.
Specifically, we follow [4]} to select \(L=32\) . As for \(T\) , we choose \(T=3\)  for UCF-Crime and \(T=7\)  for ShanghaiTech. We have shown the selection of \(\mathcal {K}\)  on ShanghaiTech with \(I3D^{RGB}\)  features in Figure REF . Additionally, \(\lambda \)  is set as 0.01. 40 normal and 40 abnormal videos are randomly sampled as a batch when training.
